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首页> 外文期刊>Chemistry central journal >PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set
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PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

机译：来自巴里省空气质量监测网络的PM 10 和气态污染物趋势：两年半数据集的主成分分析和绝对主成分评分

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Background The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it’s referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS). Results Comparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors. The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants. Conclusions The analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in order to reach the WHO recommended levels.
机译：背景技术气溶胶的化学成分和粒径分布是影响空气质量的最重要因素。特别是，暴露于较细的颗粒会导致对人体健康的短期和长期影响。在本文中，PM10（空气动力学直径小于10μm的颗粒物）显示了在巴里省六个监测站中监测到的CO，NOx（NO和NO2），苯和甲苯的趋势。所使用的数据集由所有参数的每两小时平均值（每个参数每天12次每两小时平均值）组成，是指从2005年1月到2007年5月的时间段。本文的主要目的是提供清楚地说明了监控站的大量数据可以提供有关污染物源数量和性质的信息，并且主要通过使用多元统计技术（例如主成分分析（PCA））来评估交通源对PM10浓度水平的贡献）和绝对主成分评分（APCS）。结果比较每个参数的昼夜平均浓度（每天），已经指出，某些参数（例如CO，苯和甲苯）的昼夜行为与PM10不同。这表明CO，苯和甲苯的浓度主要与运输系统有关，而PM10主要受不同因素的影响。统计技术确定了与车辆交通和颗粒物运输相关的三个经常性来源，涵盖了超过90％的差异。同时分析气体和PM10可以突显这些污染物的来源之间的差异。结论通过对大数据集污染物趋势的分析以及PCA和APCS等多元统计技术的应用，可以提供有关空气质量和污染物来源的有用信息。这些知识可以为环境政策提供有用的建议，以达到WHO推荐的水平。

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